Menzies Centre for Health Policy, School of Public Health, University of Sydney, Level 6, Charles Perkins Centre, Camperdown, Australia.
(Then) Family Medicine Research Centre, School of Public Health, University of Sydney, Camperdown, Australia.
BMC Fam Pract. 2019 Feb 14;20(1):28. doi: 10.1186/s12875-019-0914-y.
The ageing population and increasing prevalence of multimorbidity place greater resource demands on the health systems internationally. Accurate prediction of general practice (GP) services is important for health workforce planning. The aim of this research was to develop a parsimonious model that predicts patient visit rates to general practice.
Between 2012 and 2016, 1449 randomly selected Australian GPs recorded GP-patient encounter details for 43,501 patients in sub-studies of the Bettering the Evaluation and Care of Health (BEACH) program. Details included patient characteristics, all diagnosed chronic conditions per patient and the number of GP visits for each patient in previous 12 months. BEACH has a single stage cluster design. Survey procedures in SAS version 9.3 (SAS Inc., Cary, NC, USA) were used to account for the effect of this clustering. Models predicting patient GP visit rates were tested. R-square value was used to measure how well each model predicts GP attendance. An adjusted R-square was calculated for all models with more than one explanatory variable. Statistically insignificant variables were removed through backwards elimination. Due to the large sample size, p < 0.01 rather than p < 0.05 was used as level of significance.
Number of diagnosed chronic conditions alone accounted for 25.48% of variance (R-square) in number of visits in previous year. The final parsimonious model accounted for 27.58% of variance and estimated that each year: female patients had 0.52 more visits; Commonwealth Concessional Health Care Card holders had 1.06 more visits; for each chronic condition patients made 1.06 more visits; and visit rate initially decreased with age before increasing exponentially.
Number of diagnosed chronic conditions was the best individual predictor of the number of GP visits. Adding patient age, sex and concession card status explained significantly more variance. This model will assist health care planning by providing an accurate prediction of patient use of GP services.
人口老龄化和多种疾病的患病率不断上升，使国际卫生系统面临更大的资源需求。准确预测全科医生（GP）服务量对于卫生人力资源规划至关重要。本研究旨在开发一个简洁的模型，预测全科医生就诊率。
2012 年至 2016 年期间，澳大利亚的 1449 名随机选择的全科医生在 BEACH 项目的子研究中记录了 43501 名患者的全科医生-患者就诊详细信息。详细信息包括患者特征、每位患者的所有诊断慢性疾病以及每位患者在过去 12 个月的就诊次数。BEACH 采用单阶段聚类设计。SAS 版本 9.3（SAS Inc.，美国 Cary）中的调查程序用于考虑这种聚类的影响。测试了预测患者 GP 就诊率的模型。R 平方值用于衡量每个模型对 GP 就诊的预测能力。对于具有多个解释变量的所有模型，计算了调整后的 R 平方。通过向后消除去除不显著的变量。由于样本量大，p<0.01 而不是 p<0.05 被用作显著水平。
仅诊断出的慢性疾病数量就占前一年就诊次数方差（R 平方）的 25.48%。最终的简洁模型占 27.58%的方差，估计每年：女性患者就诊次数增加 0.52 次；持有联邦优惠医疗保健卡的患者就诊次数增加 1.06 次；每位患者的慢性疾病就诊次数增加 1.06 次；就诊率先随年龄增加而下降，然后呈指数增长。
诊断出的慢性疾病数量是预测 GP 就诊次数的最佳个体预测指标。增加患者年龄、性别和优惠卡状态可以显著增加方差解释。该模型通过提供对患者使用 GP 服务的准确预测，将有助于医疗保健规划。
